An Analysis of Pre-Infection Detection Techniques for Botnets and other Malware

Traditional techniques for detecting malware, such as viruses, worms and rootkits, rely on identifying virus-specific signature definitions within network traffic, applications or memory. Because a sample of malware is required to define an attack signature, signature detection has drawbacks when accounting for malware code mutation, has limited use in zero-day protection and is a post-infection technique requiring malware to be present on a device in order to be detected. A malicious bot is a malware variant that interconnects with other bots to form a botnet. Amongst their multiple malicious uses, botnets are ideal for launching mass Distributed Denial of Services attacks against the ever increasing number of networked devices that are starting to form the Internet of Things and Smart Cities. Regardless of topology; centralised Command & Control or distributed Peer-to-Peer, bots must communicate with their commanding botmaster. This communication traffic can be used to detect malware activity in the cloud before it can evade network perimeter defences and to trace a route back to source to takedown the threat. This paper identifies the inefficiencies exhibited by signature-based detection when dealing with botnets. Total botnet eradication relies on traffic-based detection methods such as DNS record analysis, against which malware authors have multiple evasion techniques. Signature-based detection displays further inefficiencies when located within virtual environments which form the backbone of data centre infrastructures, providing malware with a new attack vector. This paper highlights a lack of techniques for detecting malicious bot activity within such environments, proposing an architecture based upon flow sampling protocols to detect botnets within virtualised environments

The Malware Analysis Body of Knowledge (MABOK)

The ability to forensically analyse malicious software (malware) is becoming an increasingly important discipline in the field of Digital Forensics. This is because malware is becoming stealthier, targeted, profit driven, managed by criminal organizations, harder to detect and much harder to analyse. Malware analysis requires a considerable skill set to delve deep into malware internals when it is designed specifically to detect and hinder such attempts. This paper presents a foundation for a Malware Analysis Body of Knowledge (MABOK) that is required to successfully forensically analyse malware. This body of knowledge has been the result of several years of research into malware dissection

A methodology for testing virtualisation security

There is a growing interest in virtualisation due to its central role in cloud computing, virtual desktop environments and Green IT. Data centres and cloud computing utilise this technology to run multiple operating systems on one physical server, thus reducing hardware costs. However, vulnerabilities in the hypervisor layer have an impact on any virtual machines running on top, making security an important part of virtualisation. In this paper, we evaluate the security of virtualisation, including detection and escaping the environment. We present a methodology to investigate if a virtual machine can be detected and further compromised, based upon previous research. Finally, this methodology is used to evaluate the security of virtual machines. The methods used to evaluate the security include analysis of known vulnerabilities and fuzzing to test the virtual device drivers on three different platforms: VirtualBox, Hyper-V and VMware ESXI. Our results demonstrate that the attack surface of virtualisation is more prone to vulnerabilities than the hypervisor. Comparing our results with previous studies, each platform withstood IOCTL and random fuzzing, demonstrating that the platforms are more robust and secure than previously found. By building on existing research, the results show that security in the hypervisor has been improved. However, using the proposed methodology in this paper it has been shown that an attacker can easily determine that the machine is a virtual machine, which could be used for further exploitation. Finally, our proposed methodology can be utilised to effectively test the security of a virtualised environment

MalwareLab: Experimentation with Cybercrime Attack Tools

Cybercrime attack tools (i.e. Exploit Kits) are reportedly responsible for the majority of attacks affecting home users. Exploit kits are traded in the black markets at different prices and advertising different capabilities and functionalities. In this paper we present our experimental approach in testing 10 exploit kits leaked from the markets that we deployed in an isolated environment, our MalwareLab. The purpose of this experiment is to test these tools in terms of resiliency against changing software configurations in time. We present our experiment design and implementation, discuss challenges, lesson learned and open problems, and present a preliminary analysis of the results

Practical Experiences of Building an IPFIX Based Open Source Botnet Detector

The academic study of flow-based malware detection has primarily focused on NetFlow v5 and v9. In 2013 IPFIX was ratified as the flow export standard. As part of a larger project to develop protection methods for Cloud Service Providers from botnet threats, this paper considers the challenges involved in designing an open source IPFIX based botnet detection function. This paper describes how these challenges were overcome and presents an open source system built upon Xen hypervisor and Open vSwitch that is able to display botnet traffic within Cloud Service Provider-style virtualised environments. The system utilises Euler property graphs to display suspect “botnests”. The conceptual framework presented provides a vendor-neutral, real-time detection mechanism for monitoring botnet communication traffic within cloud architectures and the Internet of Things

The Malware Analysis Body of Knowledge (MABOK)

The ability to forensically analyse malicious software (malware) is becoming an increasingly important discipline in the field of Digital Forensics. This is because malware is becoming stealthier, targeted, profit driven, managed by criminal organizations, harder to detect and much harder to analyse. Malware analysis requires a considerable skill set to delve deep into malware internals when it is designed specifically to detect and hinder such attempts. This paper presents a foundation for a Malware Analysis Body of Knowledge (MABOK) that is required to successfully forensically analyse malware. This body of knowledge has been the result of several years of research into malware dissection

Malware Detection in Cloud Computing Infrastructures

Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions. Index Terms—Security, resilience, invasive software, multi-agent systems, network-level security and protection

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

On the placement of security-related Virtualised Network Functions over data center networks

Middleboxes are typically hardware-accelerated appliances such as firewalls, proxies, WAN optimizers, and NATs that play an important role in service provisioning over today's data centers. Reports show that the number of middleboxes is on par with the number of routers, and consequently represent a significant commitment from an operator's capital and operational expenditure budgets. Over the past few years, software middleboxes known as Virtual Network Functions (VNFs) are replacing the hardware appliances to reduce cost, improve the flexibility of deployment, and allow for extending network functionality in short timescales. This dissertation aims at identifying the unique characteristics of security modules implementation as VNFs in virtualised environments. We focus on the placement of the security VNFs to minimise resource usage without violating the security imposed constraints as a challenge faced by operators today who want to increase the usable capacity of their infrastructures. The work presented here, focuses on the multi-tenant environment where customised security services are provided to tenants. The services are implemented as a software module deployed as a VNF collocated with network switches to reduce overhead. Furthermore, the thesis presents a formalisation for the resource-aware placement of security VNFs and provides a constraint programming solution along with examining heuristic, meta-heuristic and near-optimal/subset-sum solutions to solve larger size problems in reduced time. The results of this work identify the unique and vital constraints of the placement of security functions. They demonstrate that the granularity of the traffic required by the security functions imposes traffic constraints that increase the resource overhead of the deployment. The work identifies the north-south traffic in data centers as the traffic designed for processing for security functions rather than east-west traffic. It asserts that the non-sharing strategy of security modules will reduce the complexity in case of the multi-tenant environment. Furthermore, the work adopts on-path deployment of security VNF traffic strategy, which is shown to reduce resources overhead compared to previous approaches

Identification of potential malicious web pages

Malicious web pages are an emerging security concern on the Internet due to their popularity and their potential serious impact. Detecting and analysing them are very costly because of their qualities and complexities. In this paper, we present a lightweight scoring mechanism that uses static features to identify potential malicious pages. This mechanism is intended as a filter that allows us to reduce the number suspicious web pages requiring more expensive analysis by other mechanisms that require loading and interpretation of the web pages to determine whether they are malicious or benign. Given its role as a filter, our main aim is to reduce false positives while minimising false negatives. The scoring mechanism has been developed by identifying candidate static features of malicious web pages that are evaluate using a feature selection algorithm. This identifies the most appropriate set of features that can be used to efficiently distinguish between benign and malicious web pages. These features are used to construct a scoring algorithm that allows us to calculate a score for a web page's potential maliciousness. The main advantage of this scoring mechanism compared to a binary classifier is the ability to make a trade-off between accuracy and performance. This allows us to adjust the number of web pages passed to the more expensive analysis mechanism in order to tune overall performance